Menjadwalkan update OS otomatis instance. Hal ini mengaktifkan
layanan upgrade
tanpa pengawasan Debian dan hanya berlaku untuk image berbasis VM.
install-unattended-upgrades
true: Mengaktifkan update OS otomatis.
false (default): Menonaktifkan update OS otomatis.
Pengguna Jupyter kustom
Menentukan nama pengguna Jupyter default. Setelan ini
menentukan nama folder untuk notebook Anda. Misalnya,
daripada direktori /home/jupyter/ default, Anda
dapat mengubah direktori menjadi /home/CUSTOM_NAME.
Kunci metadata ini tidak memengaruhi akses ke instance.
jupyter-user
String. Nilai defaultnya adalah jupyter.
Mendownload file
Memungkinkan Anda mendownload file dari JupyterLab.
notebook-disable-downloads
true: Menonaktifkan download file.
false (default): Mengaktifkan download file.
Akses root
Mengaktifkan akses root.
notebook-disable-root
true: Menonaktifkan akses root.
false (default): Mengaktifkan akses root.
Akses terminal
Mengaktifkan akses terminal.
notebook-disable-terminal
true: Menonaktifkan akses terminal.
false (default): Mengaktifkan akses terminal.
Upgrade terjadwal
Menjadwalkan upgrade otomatis instance.
notebook-upgrade-schedule
Jadwal mingguan atau bulanan yang Anda tetapkan, dalam
format
unix-cron, misalnya, 00 19 * * MON berarti setiap minggu pada
hari Senin, pukul 1900 Waktu Greenwich (GMT).
Fitur ini dinonaktifkan secara default.
Skrip pasca-startup
Menjalankan skrip kustom setelah startup.
post-startup-script
URI skrip pasca-startup di Cloud Storage, misalnya:
gs://bucket/hello.sh. Fitur ini dinonaktifkan secara default.
Perilaku skrip pasca-startup
Menentukan kapan dan bagaimana skrip pasca-startup berjalan.
post-startup-script-behavior
run_once (default): Menjalankan skrip pasca-startup
sekali setelah pembuatan atau upgrade instance.
run_every_start: Menjalankan skrip post-startup
setelah setiap kali memulai.
download_and_run_every_start: Mendownload ulang
skrip pasca-startup dari sumbernya, lalu menjalankan skrip setelah
setiap kali dimulai.
Melaporkan kondisi acara
Memeriksa responsivitas setiap 30 detik untuk metrik VM.
report-event-health
true (default): Mengaktifkan pelaporan kondisi peristiwa.
Mengaktifkan JupyterLab 4
(Pratinjau)
di instance Anda. Untuk mengetahui informasi selengkapnya, lihat
Pratinjau JupyterLab 4.
enable-jupyterlab4-preview
true: Mengaktifkan JupyterLab 4.
false (default): Mengaktifkan JupyterLab 3.
Metadata yang dikelola oleh Compute Engine
Beberapa kunci metadata telah ditentukan sebelumnya oleh Compute Engine. Untuk mengetahui
informasi selengkapnya, lihat
Kunci metadata
standar.
Kunci metadata yang dilindungi
Beberapa kunci metadata dicadangkan hanya untuk penggunaan sistem. Jika Anda menetapkan
nilai ke kunci metadata ini, nilai baru akan ditimpa oleh
nilai sistem.
Kunci metadata yang dicadangkan mencakup, tetapi tidak terbatas pada:
data-disk-uri
enable-oslogin
framework
notebooks-api
notebooks-api-version
nvidia-driver-gcs-path
proxy-url
restriction
shutdown-script
title
version
Membuat instance dengan metadata tertentu
Anda dapat membuat instance Vertex AI Workbench dengan metadata tertentu
menggunakan Google Cloud konsol, Google Cloud CLI,
Terraform, atau Notebooks API.
Konsol
Saat membuat instance Vertex AI Workbench, Anda dapat menambahkan
metadata di bagian Lingkungan pada Opsi lanjutan.
gcloud
Saat membuat instance Vertex AI Workbench, Anda dapat menambahkan
metadata menggunakan perintah berikut:
Gunakan metode instances.patch
dengan nilai metadata yang ditetapkan ke string kosong dan
gce_setup.metadata di updateMask untuk menghapus
fitur yang sesuai.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[],[],null,["# Manage features through metadata\n================================\n\nThis page describes how to manage some Vertex AI Workbench instance features\nby modifying the instance's metadata key-value pairs.\n\nMetadata keys\n-------------\n\nFor information about features and their respective metadata keys,\nsee the following table.\n\nMetadata managed by Compute Engine\n----------------------------------\n\nSome of the metadata keys are predefined by Compute Engine. For more\ninformation, see\n[Predefined metadata\nkeys](/compute/docs/metadata/predefined-metadata-keys).\n\nProtected metadata keys\n-----------------------\n\nSome metadata keys are reserved for system use only. If you assign\nvalues to these metadata keys, the new values will be overwritten by the\nsystem values.\n\nReserved metadata keys include and are not limited to:\n\n- `data-disk-uri`\n- `enable-oslogin`\n- `framework`\n- `notebooks-api`\n- `notebooks-api-version`\n- `nvidia-driver-gcs-path`\n- `proxy-url`\n- `restriction`\n- `shutdown-script`\n- `title`\n- `version`\n\nCreate an instance with specific metadata\n-----------------------------------------\n\nYou can create a Vertex AI Workbench instance with specific metadata\nby using the Google Cloud console, the Google Cloud CLI,\nTerraform, or the Notebooks API. \n\n### Console\n\nWhen you create a Vertex AI Workbench instance, you can add\nmetadata in the **Environment** section of **Advanced options**.\n\n### gcloud\n\nWhen you create a Vertex AI Workbench instance, you can add\nmetadata by using the following command: \n\n```bash\ngcloud workbench instances create INSTANCE_NAME --metadata=KEY=VALUE\n```\n\n### Terraform\n\nTo add metadata, create the resource with metadata key-value pairs.\n\n\u003cbr /\u003e\n\nTo learn how to apply or remove a Terraform configuration, see\n[Basic Terraform commands](/docs/terraform/basic-commands).\n\n\u003cbr /\u003e\n\n resource \"google_workbench_instance\" \"default\" {\n name = \"workbench-instance-example\"\n location = \"us-central1-a\"\n\n gce_setup {\n machine_type = \"n1-standard-1\"\n vm_image {\n project = \"cloud-notebooks-managed\"\n family = \"workbench-instances\"\n }\n metadata = {\n key = \"value\"\n }\n }\n }\n\n### Notebooks API\n\nUse the [`instances.create`](/vertex-ai/docs/workbench/reference/rest/v2/projects.locations.instances/create)\nmethod with metadata values to manage the corresponding features.\n\nUpdate an instance's metadata\n-----------------------------\n\nYou can update the metadata of a Vertex AI Workbench instance\nby using the Google Cloud console, the Google Cloud CLI,\nTerraform, or the Notebooks API. \n\n### Console\n\nTo update the metadata of a Vertex AI Workbench instance,\ndo the following:\n\n1. In the Google Cloud console, go to the **Instances** page.\n\n [Go to Instances](https://console.cloud.google.com/vertex-ai/workbench/instances)\n2. In the list of instances, click the name of the instance that you want\n to update.\n\n3. On the **Instance details** page, click **Software and security**.\n\n4. In the **Metadata** section, update the metadata key-value pairs that\n you want to change.\n\n5. Click **Submit**.\n\n### gcloud\n\nYou can update the metadata on a Vertex AI Workbench instance\nby using the following command: \n\n```bash\ngcloud workbench instances update INSTANCE_NAME --metadata=KEY=VALUE\n```\n\n### Terraform\n\nYou can change the metadata key-value pairs to manage\nthe corresponding features on Vertex AI Workbench instances.\n\n\u003cbr /\u003e\n\nTo learn how to apply or remove a Terraform configuration, see\n[Basic Terraform commands](/docs/terraform/basic-commands).\n\n\u003cbr /\u003e\n\n resource \"google_workbench_instance\" \"default\" {\n name = \"workbench-instance-example\"\n location = \"us-central1-a\"\n\n gce_setup {\n machine_type = \"n1-standard-1\"\n vm_image {\n project = \"cloud-notebooks-managed\"\n family = \"workbench-instances\"\n }\n metadata = {\n key = \"updated_value\"\n }\n }\n }\n\n### Notebooks API\n\nUse the [`instances.patch`](/vertex-ai/docs/workbench/reference/rest/v2/projects.locations.instances/patch)\nmethod with metadata values and `gce_setup.metadata` in the `updateMask`\nto manage the corresponding features.\n\nRemove metadata from an instance\n--------------------------------\n\nYou can remove metadata from a Vertex AI Workbench instance\nby using the Google Cloud console, the Google Cloud CLI,\nTerraform, or the Notebooks API. \n\n### Console\n\nTo remove metadata from a Vertex AI Workbench instance,\ndo the following:\n\n1. In the Google Cloud console, go to the **Instances** page.\n\n [Go to Instances](https://console.cloud.google.com/vertex-ai/workbench/instances)\n2. In the list of instances, click the name of the instance that you want\n to modify.\n\n3. On the **Instance details** page, click **Software and security**.\n\n4. In the **Metadata** section, to the right of a key-value pair that\n you want to delete, click\n delete **Delete**.\n\n5. Click **Submit**.\n\n### gcloud\n\nYou can remove metadata from a Vertex AI Workbench instance\nby using the following command: \n\n```bash\ngcloud workbench instances update INSTANCE_NAME --metadata=KEY\n```\n\n### Terraform\n\nYou can remove metadata key-value pairs to manage the\ncorresponding features of a Vertex AI Workbench instance.\n\n\u003cbr /\u003e\n\nTo learn how to apply or remove a Terraform configuration, see\n[Basic Terraform commands](/docs/terraform/basic-commands).\n\n\u003cbr /\u003e\n\n resource \"google_workbench_instance\" \"default\" {\n name = \"workbench-instance-example\"\n location = \"us-central1-a\"\n\n gce_setup {\n machine_type = \"n1-standard-1\"\n vm_image {\n project = \"cloud-notebooks-managed\"\n family = \"workbench-instances\"\n }\n metadata = {\n }\n }\n }\n\n### Notebooks API\n\nUse the [`instances.patch`](/vertex-ai/docs/workbench/reference/rest/v2/projects.locations.instances/patch)\nmethod with the metadata value set to an empty string and\n`gce_setup.metadata` in the `updateMask` to remove the\ncorresponding feature."]]
